Data Analytics A.B.
Chair Department of Mathematics and Statistics: Associate Professor Beuerle
Associate Chair: Professor L. Taylor
The Department of Mathematics and Statistics offers programs leading to the Bachelor of Arts or Bachelor of Science degree with a major in Applied Mathematics, Data Analytics, Mathematics or Statistics. With an A.B. in Data Analytics students will be exposed to methods and issues related to managing and analyzing data. This particular degree is meant to be interdisciplinary in nature with requirements in mathematics, statistics, computer science, media analytics, and a requirement of a supporting additional major or minor.
Foundations
The following courses are required
| STS 2120 | STATISTICS IN APPLICATION | 4 sh |
| STS 2320 | STATISTICAL MODELING | 4 sh |
| STS 3270 | STATISTICAL COMPUTING FOR DATA MANAGEMENT | 4 sh |
| STS 3470 | STATISTICAL COMPUTING FOR SIMULATION AND THEORY | 4 sh |
| CSC 1100 | DATA SCIENCE AND VISUALIZATION | 4 sh |
| MTH 2300 | MATHEMATICAL METHODS FOR DATA ANALYTICS | 4 sh |
| STS 3300 | STATISTICAL METHODS FOR DATA ANALYTICS | 4 sh |
Capstone Requirement
Students will be required to complete either STS 4980 or approved capstone focusing on data analytics from another major or minor.
Electives
Students should take 2 of the following courses
Note: Some classes have additional pre-reqs not met by the foundations, these are noted in parentheses below.
- CSC 3211: Database Systems (pre-req: CSC 1300)
- CSC 4422: Data Mining and Machine Learning (pre-req: CSC 3211 and either CSC 2300 or instructor permission)
- ECO 4400: Economic Consulting (pre-req: ECO 3200, ECO 3300 and ECO 3120 or ECO 3100)
- MGT 3100: Big Data Analysis (pre-req: BUS 2110)
- MGT 4110: Data Wrangling (pre-req: MGT 3100)
- MGT 4250: Data Visualization and Storytelling (pre-req: MGT 3100 and MGT 3230)
- MGT 4260: Data Mining for Managerial Decision Making (pre-req: BUS 2110 and MGT 3230
- PST 3010: Policy Analysis (pre-req: PST 2250)
Additional Requirements
Students must complete a full minor or a second major in another discipline. A major in statistics with a concentration in data analytics or minor in computer science, data science, or statistics does not count toward fulfillment of this requirement.
Students should be able to construct models to analyze data using common data analytics approaches.
Students should be able to use at least two different statistical software or programming languages to manage, analyze, or present data.
Students should be able to design computationally reproducible data management and analysis workflows using best practices.
Students should be able to evaluate the importance of data analytics and the role it plays in making informed, data-driven decisions in at least one discipline beyond Data Analytics.
Students should be able to discuss current ethical issues in data, such as data collection, management, analysis, and interpretation.
Students should be able to articulate data analytics ideas, methods, and results to technical and non-technical audiences.
Total Credit Hours: 40